Impact of dual media on misinformation spread in heterogeneous online social networks

Abstract

The rapid spread of misinformation through online social networks presents a significant threat to social stability, especially during public crises. This study develops a Susceptible–Asymptomatic–Infected–Recovered (SAIR) model for misinformation spread. It incorporates dual media coverage and network heterogeneity, and addresses key limitations of existing models that assume network degree-homogeneity and oversimplify media effects. The model includes both positive media reports (fact-checking) represented by saturation functions and the amplification of negative media within degree-heterogeneous networks. Using mean-field theory and next-generation matrix methods, the basic reproduction number is derived and the stability and existence conditions of both misinformation-free and misinformation-endemic equilibria are established. Numerical simulations show that positive media reports reduce the peak and final scale of misinformation by 11% and 18%, respectively, while hub nodes accelerate early-stage misinformation spread. Sensitivity analysis identifies critical factors influencing misinformation dynamics, highlighting the importance of targeted interventions on high-degree nodes. These results offer valuable insights for policymakers to design intervention strategies that leverage media polarity regulation and mitigate network vulnerabilities.